Interview: 5 questions about open analytics

By Alison Bolen, SAS Insights Editor

When you need to price vacation rentals for 3.7 million vacation members who have access to 2 million distinct SKUs, you need accurate, reliable, nonstop analytics.

At Wyndham Destination Network, the analytics team uses a combination of open source and enterprise analytics to find innovative solutions to complex problems from pricing elasticity to resort operations and customer retention.

Recently, we talked to Jeremy TerBush, Senior Vice President of Analytics at Wyndham Destination Network, about his team’s use of analytics, ranging from open source technologies to enterprise solutions.

So we’ll do the forecasting with open analytics as an initial prototype. When we’re happy with that first stage, we run it in SAS. For us, it’s easier to create an industrial-strength product in SAS.

How do you define open source vs. open software?

Jeremy TerBush: Open source to me means I can download the system right now and create a program directly on my machine without any licensing. It’s free, easy to use, and there are a lot of communities out there talking about it and working on improving the software.

Open software, on the other hand, is software that makes it easy to connect with other pieces of software. We have systems that produce forecasts, and they need to send those forecasts over to another routine. Open software would allow that to happen seamlessly and easily from a development and execution perspective.

How have you embraced openness at Wyndham?

TerBush: We don’t mandate that everyone use the same tools. When people are coming across a new problem, and it’s not part of an implemented system we’ve already built, we’ve seen our analysts turn to more of the open tools. We’ve had a lot of success developing and solving smaller scale projects with open source software. Now, if we find need to operationalize that work, typically we find a way to move it into our larger environment and find a way to use SAS®.

What we have is somewhat of an open ecosystem because we allow the group to use whatever tools it wants given the problem at hand. Open source is used in the prototyping stage for one-off problems. That’s where we’ve found a lot of success using open source technology.

When would you switch from open source technology to enterprise software?

TerBush: The processes we’re setting up that need to be run daily are typically pricing processes, like forecasting demand and price elasticity for our rental units. To do that, we have to take all the recent historical and transaction behavior and feed it into a forecasting routine to ultimately recommend a daily price for that unit. That model includes optimization and forecasting that were prototyped with open source analytics software. When we were happy with the results, we moved the algorithm to SAS for daily scheduling.

If it’s a job that has to run on a daily basis for pricing forecasts, I don’t have the confidence that any open implementation would give the team what it needs on a daily basis to optimize prices. So we’ll do the forecasting with open analytics as an initial prototype. When we’re happy with that first stage, we run it in SAS. For us, it’s easier to create an industrial-strength product in SAS.

What are the benefits of using both?

TerBush: One of the main benefits of using both SAS and open source software is that you can let people use the platform they’re most comfortable with. It’s a learning curve for them to completely shift their thinking. To allow everyone to use whatever platform they’re comfortable with is a big productive boost for us. I think we get better answers too. We can collect the best practices and draw from communities of all platforms and have a bigger pool of ideas. It gives us more flexibility within the team to solve the problem in a number of different ways.

What other technologies are important for remaining open?

TerBush: Within our organization and across other organizations, it’s becoming much easier for companies to share data by developing common-sense APIs. For example, we did some work recently where we needed to use the Google Maps API, and we went to Python immediately.

We’re seeing more and more that there’s external data that we can tap into. When building solutions internally, we want to communicate our models to multiple platforms and send results to APIs to whatever platform needs to consume it. That’s definitely the way things are going: being able to produce results so any platform can consume them, and APIs are the way to do it.